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create_object

Create primitive 3D objects such as cubes, spheres, cylinders, and torus. Specify type, name, location, rotation, and scale to generate objects in a scene.

Instructions

Create a primitive object in the scene.

Args: type: Primitive type. One of: CUBE, SPHERE, UV_SPHERE, ICO_SPHERE, CYLINDER, CONE, TORUS, PLANE, CIRCLE, MONKEY, EMPTY. name: Optional name for the object. Auto-generated if empty. location: XYZ position as a 3-element list/tuple. Defaults to origin. rotation: XYZ Euler rotation in radians as a 3-element list/tuple. scale: XYZ scale as a 3-element list/tuple. Defaults to (1,1,1).

Returns: Dict with the created object's name, type, and location.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYes
nameNo
locationNo
rotationNo
scaleNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It describes the creation action and return value, but lacks details on side effects (e.g., selection, collection placement, naming conflict behavior). It is adequate but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured as a docstring with clear sections for args and returns. It is appropriately sized and front-loaded with the main purpose. Every sentence is relevant, though minor redundancy in listing types could be trimmed.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no annotations, and an output schema, the description covers the parameters and return value well. It could mention that the object is added to the active collection or scene, but overall it provides sufficient context for an AI agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, meaning the schema provides no descriptions. The description adds full parameter semantics: explains each parameter's purpose, defaults, and constraints (e.g., rotation in radians, scale defaults to (1,1,1)). This adds significant value beyond the raw schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Create a primitive object in the scene' and lists specific primitive types, which distinguishes it from sibling tools like create_camera or create_light. However, it does not explicitly contrast with other creation tools, so it is very clear but not perfectly differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description implies it is for primitive objects, but given many sibling creation tools (e.g., create_camera, create_curve), it should provide more explicit when-to-use instructions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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